The present disclosure relates generally to systems and methods for determining a medical condition of a patient using imaging data and, in particular, to systems and methods for identifying the presence and/or progression of a brain condition of a subject.
Neurodegenerative diseases and other syndromes affecting the brain commonly exhibit distinctive symptoms and characteristics that often become more pronounced as disease severity increases. During diagnosis, specific signatures visible on various imaging modalities are utilized to determine the presence and state of the disease. For instance, with glucose being a primary source of energy for neuronal activity, measurement of glucose metabolism using positron emission tomography (“PET”) imaging has been demonstrated to provide measures of neuronal decline in affected brain areas for a variety of neurodegenerative diseases and syndromes. Similarly, as neurons degrade, brain volume decreases according to characteristic patterns, which is measurable using magnetic resonance imaging (“MRI”). Other imaging modalities, such as functional MRI and single photon emission computed tomography (“SPECT”), as well as other biomarkers, including imaging amyloid plaques and tangles, have also been used to measure brain function for diagnosing and treating neurodegenerative diseases.
PET imaging technology measures chemical or functional activity in the brain by detecting gamma rays emitted by the decay of radioactive tracers injected into a patient. The greater the amount of the target of interest, the greater the binding of the tracer to that target, and the hence greater and more pervasive the signal intensity detected. Various tracers have been developed that bind to different targets in the brain, such as neurotransmitter receptors or amyloid plaque. For instance, common tracers used for imaging amyloid plaques include 11C-PiB (Pittsburgh Compound B), florbetapir (Lilly/Avid), flutemetamol (GE Healthcare), florbetaben (Piramal), and others.
In patients with Alzheimer's Disease (“AD”), amyloid plaque is the result of accumulation of abnormally cleaved proteins that cluster together outside nerve cells or neurons, leading to disruptions in neural communication and function, as well as cell death. Therefore, the presence of amyloid plaques is part of the diagnostic criteria for AD, and amyloid imaging has been incorporated into many clinical trials. As an example, FIGS. 1A and 1B show transaxial and sagittal amyloid PET images from two different patients, illustrating different radiotracer activity patterns in the brain. In particular, FIG. 1A shows an amyloid negative scan, while FIG. 1B shows an amyloid positive scan, as visible from the increased signal intensities relative to FIG. 1A. In general, amyloid plaque accumulates in gray matter (“GM”) regions, as indicated by arrows 100 in the positive scan of FIG. 1B. However, many amyloid PET tracers can also bind to white matter (“WM”) as well, as appreciated by non-specific white matter binding around the ventricles in the middle of the brain in FIGS. 1A and 1B, and indicated by arrows 102. This may be due either to slower clearance of the tracer from WM, non-specific binding to WM, or specific binding to a non-amyloid entity such as myelin. Hence, specific binding to amyloid in gray matter tissue in the amyloid-positive
Data collected by a PET scanner can be assembled into a series of images or frames, each representing radioactivity detected over a specific time window after tracer administration. The duration of each window may depend on the time elapsed from tracer injection. For instance, early images obtained shortly following radiotracer injection may be acquired over windows lasting a few seconds in order to capture rapid changes in emitted signal, while later images can be acquired over windows lasting from several minutes up to 30 minutes or even longer. In some cases, subjects may move during a scan, and so later images may be acquired over shorter windows (for example, 5 minutes each) so that corrections can be made for motion. The time course of radioactivity captured by the series of images may then be used to form “time activity curves” for different regions of interest (“ROIs”), as shown in the example of FIG. 2. Specifically, in the initial stage 200 following tracer injection, signal intensity can increase quickly, largely reflecting tracer influx from plasma into tissue, and can be highly correlated with regional cerebral blood flow rate (“rCBF”). Following a non-equilibrium stage 202 during which the tracer accumulates in target-rich regions, an equilibrium stage 204 is then reached. The time to reach equilibrium can vary depending upon the particular tracer, the region of accumulation, and target load. For example, the equilibrium for florbetapir occurs at about 40-45 minutes, for 11C-PiB at about 45 minutes, and for flutemetamol at about 80 minutes.
In analyzing PET images to determine amyloid burden, kinetic modeling methods are often used. These typically techniques use the entire time activity curve and blood measurements, and solve a set of differential equations characterizing tracer influx and efflux from various tissue compartments. In this manner, blood flow rate, clearance rate, and tracer binding can be quantified. In some simplified models, time activity curve ratios between target regions and regions in which tracer binding is negligible are computed to eliminate the discomfort and logistical issues associated with arterial blood sampling. This produces a Binding Potential (“BP”) value that when added to 1 produces a Distribution Volume Ratio (“DVR”), which indicates amyloid burden. The DVR is typically compared to a threshold value to establish whether a patient is considered to be positive or negative for amyloid plaque.
Some kinetic models have demonstrated better robustness, noise, and bias characteristics compared to others. Also, some models have been shown to be better suited for dissociating PET signal contributions from blood flow and rate of tracer clearance, compared to the standardized uptake value ratio (“SUVR”) method, for instance, as described below. However, complete activity information from the time of injection until equilibrium is often needed, requiring appreciable patient scanning. Attempts to apply kinetic modeling using activity curves where equilibrium has not been reached, for instance in the first 20 minutes of a florbetapir scan, have resulted in failure.
Since it is often not practical to keep a patient in the scanner for imaging lasting 45 to 90 minutes, alternate amyloid burden measures are often used. In SUVR techniques, for instance, amyloid burden is often determined using later images. Typical later images are acquired 50 to 70 minutes post injection for florbetapir, or 80 to 120 minutes post injection for flutemetamol. The SUVR is then computed by dividing the mean signal intensity of a region or volume of interest (“VOI”) by the average intensity of a reference region. Typical VOIs used for determining amyloid plaque burden include the frontal cortex or anterior cingulate, while reference regions include the gray cerebellum, where amyloid plaque is not known to accumulate.
By way of example, FIG. 3 shows amyloid PET images highlighting typical VOIs (e.g. the anterior cingulate, posterior cingulate and precuneus) in a first image 300, and a reference region (e.g. gray cerebellum) in the second image 302. To support a diagnosis of AD or inclusion in a clinical trial, computed SUVR values are compared to a threshold to determine whether a patient is positive or negative. In some approaches, a “cortical average” is often calculated using four to six individual VOIs that are highly correlated, such as the frontal, anterior and posterior cingulate, lateral temporal, and parietal regions.
Although high correlation has been shown between SUVR and DVR measurements, with both resulting in important discrimination between AD patients and normal controls, and modest but positive mean rates for amyloid accumulation over time, these have a number of drawbacks. For instance, as described, DVR measurements require long acquisitions, starting immediately post-tracer injection and extending toward tracer equilibrium, to obtain full time activity curves. On the other hand, SUVR measurements do not take into consideration early information, and do not distinguish contributions of blood flow and clearance. Because of this and other factors, SUVRs often overestimate amyloid burden, a bias that increases with later images due to a lack of true equilibrium between plasma and tissue as the tracer clears from plasma. If tracer delivery and clearance were similar within a subject group, this bias would result in a simple scalar difference in empirically generated thresholds for SUVR versus DVR values. However, it is known that different subjects exhibit differences in longitudinal changes in blood flow and/or clearance, complicating analysis.
A study in AD, mild cognitive impairment (“MCI”), and healthy controls using 11C-PiB showed that while longitudinal reductions in late image SUVR values were observed in AD subjects, there was little or no change when dynamic modeling was used. The decline in SUVR values in AD subjects paralleled a decrease in the uptake rate of the tracer from blood into brain: detected by the k1′ rate constant derived from kinetic modeling but not from SUVR calculations. Thus, apparent decreases in SUVR values may be driven by reductions in cerebral blood flow in AD, which is a consideration for longitudinal studies following disease progression. Despite these limitations of the SUVR approach for the purpose of detecting whether a patient is “amyloid positive,” and for the purpose of measuring longitudinal change in large multi-site populations, the SUVR method remains the most broadly applied technique.
Therefore, given the above, there is a need for improved systems and methods for determining the presence and progression of a brain condition of a subject using PET images.